Title: A reconstruction algorithm for human action based on LLE and KRR

Authors: Jian Liu; Zhi-heng Gong; Jing Dai; Mei-ju Liu; En-yang Gao; Cheng-dong Wu

Addresses: Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, Liaoning, 110168, China ' Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, Liaoning, 110168, China ' Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, Liaoning, 110168, China ' Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, Liaoning, 110168, China ' Faculty of Information and Control Engineering, Shenyang Jianzhu University, Shenyang, Liaoning, 110168, China ' Faculty of Information Science and Engineering, Northeastern University, Shenyang, Liaoning, 110004, China

Abstract: Reconstruction of human action plays an important role in the field of image processing. In this paper, a new algorithm based on locally linear embedding (LLE) and kernel ridge regression (KRR) is proposed for the reconstruction of human action. On one hand, the images of colour and depth information are used to extract the human skeleton so that the training set of actions can be established. On the other hand, the training set is turned into an action vector library. Combined with the LLE algorithm, the human action of low-dimensional manifolds can be evaluated and the data of manifolds can be analysed. Furthermore, the human action can be reconstructed according to the KRR algorithms while the predicted convergence points of low-dimensional are inverse-mapped back to the high-dimensional Euclidean space. A lot of real experiments have been done to show that the proposed method of reconstruction of human action is greatly effective.

Keywords: image processing; locally linear embedding; LLE; human action reconstruction; kernel ridge regression; KRR; manifold; identification; human actions; human skeleton; training sets; action vector library.

DOI: 10.1504/IJMIC.2013.057133

International Journal of Modelling, Identification and Control, 2013 Vol.20 No.3, pp.215 - 222

Published online: 27 Sep 2014 *

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